Recist assessment of tumour progression

ABSTRACT

The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. 
     According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.

FIELD OF THE INVENTION

The present invention relates to a method and system that automaticallyfinds, segments and measures lesions in medical images following theResponse Evaluation Criteria In Solid Tumours (RECIST) protocol. Moreparticularly, the present invention produces an augmented version of aninput computed tomography (CT) scan with an added image mask for thesegmentations, 3D volumetric masks and models, measurements in 2D and 3Dand statistical change analyses across scans taken at different timepoints.

BACKGROUND TO THE INVENTION

CT scans make use of x-ray imaging from a number of different angles toproduce cross sectional slices of a scanned patient. Although CT scansprovide radiologists a useful tool to identify, locate and classifylesions in patients, it is difficult to perform volumetric measurementsof lesions from CT images in acceptable amount of time due to the sheernumber of image slices contained in each CT scan.

CT scans are usually presented in grayscale and therefore consist ofonly one colour channel. This creates problems when radiologists want toview different elements of the image as the human eye is capable ofdistinguishing a limited number of grey levels that are not sufficientto visualise all the information in a CT slice. The different elementsare presented in varying brightness-contrast settings on a number ofdifferent windows. For example, the view of skeletal element of the scanmay be best viewed in one brightness-contrast setting (called ‘window’),the view of the organs in another window, etc. Radiologists thereforehave to look at different contrast windows in order to identify andlocate lesions. It is an aim of the present invention to provide asolution to this problem.

Medical imaging devices usually encapsulate the scans into the DigitalImaging and Communications in Medicine (DICOM) format to enable moreefficient handling, storing, printing and transmitting of medicalimaging information.

The RECIST protocol is a set of rules that are used to assess thebehaviour of a tumour in a patient. The RECIST protocol also provides amethod that can be used to measure lesions, however, the current methodrequires a vast amount of input and uninformed decisions made beforetreatment from the radiologists which may result in inaccuratemeasurements, the inability to record measurements on suspect tumoursand more importantly not identifying a lesion from an image.

When assessing tumour progression, radiologists need to assess tumourburden for scans performed at different time points and compare theresults. It is critical that the same lesions are accurately identifiedat different time points and assessed for the purpose of comparison. Theco-location and comparison process is a burdensome and inherentlyinaccurate.

When assessing tumour burden, radiologists perform 1D measurements ofportions of lesions that fall into the 2D slices produced. Thismeasurement is inherently limited in how accurately it represents tumourburden provided by a single lesion.

SUMMARY OF THE INVENTION

Aspects and/or embodiments seek to provide a method of automaticallylocating and measuring lesions/tumours/growths and tumour changes inmedical images. Aspects and/or embodiments also seek to address theproblem relating to human error inaccuracy by performing identification,measurement and statistical analysis steps automatically.

According to a first aspect, there is provided a method for determiningvolumetric properties of one or more lesions in medical imagescomprising the following steps: receiving image data; determining one ormore locations of one or more lesions in the image data; creating animage segmentation (i.e. mask or contour) comprising the determined oneor more locations of the one or more lesions in the image data and usingthe image segmentation to determine a volumetric property of the lesion.

In this way, automatic detection and localisation of one or more lesionscan be performed using the image data. It can also automatically provideproperties (e.g. classification, volume, shape, size) relating to one ormore lesions without needing any human intervention. A segmentation(i.e. mask or contour) is also created in order to allow the location ofone or more lesions to be easily observed. Unlike current methods, thisenables volumetric assessment of medical image more accurately over aperiod of time which is indicative of whether a tumour is progressing orregressing.

Optionally, the step of determining one or more locations of the one ormore lesions comprises identifying a focal point of the one or morelesions. Optionally, the focal point of the one or more lesionscomprises a centre of mass for the one or more lesions.

Optionally, the step of determining one or more locations of the one ormore lesions comprises identifying a focal point of the one or moreanatomical landmarks. Optionally, the focal point of the one or moreanatomical landmarks comprises a centre of mass for the one or morelesions.

By using a focal point such as a centre of mass for the lesion oranatomical region, the assessment of the locations will be consistentthroughout scans and between scans where scans are taken at iterativetime intervals of the same patient. For example, although the tumour maygrow the centre of mass will always remain substantially constant thusthe same tumour can be identified in the same patient in different scansof the same portion of that patient's anatomy.

Optionally, the step of determining one or more locations of the one ormore lesions comprises determining the location relative to one or moreanatomical landmarks. Optionally, the one or more anatomical landmarkscomprise any one of: spine, ribs, lungs, and heart, liver, kidneys.

Optionally, the step of determining one or more locations of the one ormore lesions comprises identifying a focal point of the one or morelesions, identifying a focal point of the one or more anatomicallandmarks and determining the location of the one or more lesionsrelative to one or more anatomical landmarks. In this way, a tumour canbe localised relative to other anatomical objects.

By identifying landmarks, the relative position of landmarks to anytumours can be established and used as a way to identify the same tumourbetween scans, for example where the scans are taken at iterative timeintervals.

Optionally, the image data comprises any one of or any combination of:CT scan data; DICOM image files; a sequence of images of sequentialslices of anatomy; one or more grayscale images; demographic patientinformation; prior imaging data.

Optionally, the image data comprises one or more images, optionallywherein the one or images relate to a common portion of a common patientanatomy.

Optionally, the one or more images are captured at different times.Optionally, the one or more images comprise a plurality of 2D slices ofa 3D representation. Although the initial scan is a 3D representation ofthe patient, the method is applied to 2D slices of the initial 3D scan.

Optionally, the step of segmenting the one or more lesions to createlesion segmentation data; and storing the lesion segmentation data inthe image mask/contour.

Optionally, the image segmentation comprises creating a mask or contour.The segmentation masks can be combined to create a 3D representation ofthe tumour.

Optionally, the step of measuring the one or more lesions to createlesion measurement data; and storing the lesion segmentation andmeasurement data in connection with the image mask.

Optionally, the step of pre-processing the image data, whereinpre-processing comprises reading the image data and storing the imagedata in a memory.

Optionally, the image data is stored in memory as at least afour-dimensional tensor wherein the dimensions comprise: height, width,batch size and channels.

Optionally, the channels comprise one or more contrast windows orcontrast values.

Optionally, the step of determining one or more locations of the one ormore lesions in the image data comprises using a convolutional neuralnetwork.

Optionally, the step of determining one or more locations of the one ormore lesions in the image data comprises using a fully convolutionalneural network.

Optionally, the full convolutional neural network is trained usingbackpropagation; and/or the loss function for dense training is the sumover the spatial dimensions of the loss functions of the individualpixels.

Optionally, the method further comprises creating one or more heat mapsto indicate areas within respective one or more images of the image datahaving a high probability of being a tumour.

Optionally, the method further comprises the step of post-processing theone or more heat maps by feeding the one or more heat maps through aconditional random field method.

Optionally, the method further comprises the step of post-processing thedetermined one or more locations of the one or more lesions in the imagedata by feeding the determined one or more locations through aconditional random field method.

According to a second aspect, there is provided an apparatus operable toperform the method of any preceding claim.

According to a third aspect, there is provided a system comprising theapparatus of the preceding claim.

A further aspect relates to a computer program product operable toperform the method of any preceding claim.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a flowchart showing the methods of existing RECISTsystems; and

FIG. 2 illustrates a flowchart showing the methods of the presentinvention.

SPECIFIC DESCRIPTION

FIG. 1 depicts the method of current manual RECIST systems. As seen inthe flowchart, having performed a medical scan (CT, MRI, X-Ray) of apatient 101, the scanned images are collated in a DICOM format 102.

As previously mentioned, elements corresponding to different contrastsare displayed in windows. From this information, following the manualprocess a radiologist would select between 4 to 8 tumours 103 to analysebefore proceeding to select an appropriate method of treatment.

In accordance with the RECIST protocols the lesion (or tumour) must bemeasurable. Therefore, once the tumours are selected, a determination ismade as to whether or not the tumours can be measured by theradiologist. If they can, the measurements are recorded across onedimension by the radiologist 105. Since the information presented to theradiologist will be limited to the two-dimensional display of a screen,the measurements can only be across one dimension.

These measurements are then used to determine the treatment method 106.

After performing a treatment on the tumour a follow-up medical scan isperformed 107. The system seeks to identify the previously identifiedtumours from the new follow-up scans by repeating the aforementionedsteps 108.

If the previously identified tumour is found, using the new follow-upscan, the radiologist measures the tumour once more 109. Upon recordingthe measurements for the follow-up scan, they are compared to themeasurements of the tumour in the previous scan to determine whether thetumour size has changed 110.

At this point, if the tumour has decreased in size, the system willadvise the radiologist to proceed with the same course of treatmentuntil the patient is cured 111. However, if the tumour size has notdecreased, the system will advise pursuing an alternative treatmentmethod 112. The loop of this treatment phase will continue until thepatient is cured.

An example embodiment will now be described in reference to FIG. 2,wherein a typical implementation of the method according to at least oneembodiment is shown.

In FIG. 2, a pre-processing module 203 reads the DICOM files and loads2D image slices into the system. The image slices are loaded into a4-dimensional tensor of size [batch size, height, width, channels].

Measurements can be digitally analysed and recorded across one or moredimensions, and/or measurements can be performed volumetrically 204.

The images may be gathered at time intervals from the same patient andeach time a new scan is performed for the patient the process of theembodiment is carried out with both the historical data and the new datato assess the changes between observations, as discussed later.

Once the DICOM image has been processed the different contrast channelscorresponding to different elements of the patient scan are presented inthe same window. This allows the system to look at all the elements atthe same time. As an example, by applying different windowing levels tothe images and feeding these as separate input channels, the patient'sbones can be easily segmented from the scan.

Further context can be given to the model by adding the preceding andsubsequent slices in the imaging sequence as additional channels in theinput tensor. In this scenario, the input tensor would have threechannels, where the first channel is the previous slice, the secondchannel is the “current” slice, which corresponds to the associatedtarget mask, and the third channel is the subsequent slice. For example,as mentioned above, with the input tensor being a 4D tensor, [batchsize, height, width, channels], the channel value would be “3”, and thecorresponding mask to this input is a 4D tensor, [batch size, height,width, channels], where the channel value is “1”. The mask is thecorresponding target for the “current” slice in channel 2.

The image tensor is fed into a Fully Convolutional Network (FCN) whichcreates a heat map indicating areas where there is a high probability ofthe existence of a tumour. These lesions are then segmented.Additionally, the segmentations are post-processed by feeding it througha Conditional Random Field, which “cleans up” the segmentation, i.e.remove false positives. The heat maps created by the FCN represent aprobability map for the image.

The FCN is trained using backpropagation and forward pass through thenetwork. The loss function for dense training is the sum over spatialdimensions of the loss functions of the individual pixels.

${L(x)} = {\sum\limits_{i,j}{l^{\prime}( x_{i,j} )}}$

here L(x) is the loss over the whole image and l′(x_(i,j)) is the lossfor the pixel at i,j. This enables the system to automatically identifyone or more tumours from the image created by the system.

The loss function may be the DICE loss, which is defined as

$L_{DSC} = \frac{2\; {\sum\limits_{i}^{N}{s_{i}r_{i}}}}{{\sum\limits_{i}^{N}s_{i}} + {\sum\limits_{i}^{N}r_{i}}}$

where s_(i) and r_(i) represent the continuous values of the predictionmap ∈[0, . . . , 1] and the ground truth at each pixel i, respectively.Alternatively, a cross-entropy can be used. The cross-entropy loss forthe pixel at i,j is defined as

$L_{CE} = {- {\sum\limits_{c = 1}^{c}{y*{\log (s)}}}}$

where C is the number of classes, y∈{0,1} is the binary indicator forclass c, and s is the score for class c. The loss for the full image, x,is defined as the sum over all the losses for the pixels:

${L_{CE}(x)} = {\sum\limits_{i,j}( {- {\sum\limits_{c = 1}^{C}{y*{\log (s)}}}} )}$

After the model has been trained, lesions may be segmented by feeding animage to the model. The resulting output will be a probability map,which can be thresholded to obtain a segmentation mask or contour.

In order to identify the same lesions in a patient across differentscans, the system uses landmarks to construct a representation for thelocation of a lesion relative to the landmarks. These representationscan be compared across and examinations or scans. In this way, a lesioncan be found in a different scan even when other characteristics of thelesion (e.g. size and shape) have changed.

A lesion's location representation can be defined as a vector h∈R^(L),where R is the set of positive real numbers, and L is the number oflandmarks. For example, a location representation for a specific lesioncould be h_(i)=[0.43, 0.2, 0.98, 1.3]. Each element in the locationrepresentation is the (Euclidian) distance between a focal point of thelesion, such as the centre of mass of the lesion, and a focal point oflandmark, such as the centre of mass of the landmark. The centre of massof a lesion and/or landmark is defined as follows:

$R = {\frac{1}{n}{\sum\limits_{i = 1}^{n}r_{i}}}$

where n is the number of pixels in the volume (i.e. lesion or landmark),r_(i) is the coordinate vector for pixel i, and R is the coordinatevector for the centre of mass. The Euclidian distance between the centreof mass of the lesion, p, and the landmark, q, is then:

${d( {p,q} )} = \sqrt{\sum\limits_{i}^{n}( {p_{i} - q_{o}} )^{2}}$

The location representation vectors for two lesions across two differentexams can be compared using a similarity metric such as the cosinedistance or the Euclidian distance. When the distance is small, thelesions can be said to be the same.

With the system having identified and segmented the tumours,measurements of the or each tumour are digitally analysed and recordedacross one or more dimensions before proceeding to a treatment phase204. This should substantially eliminate the possibilities of humanerror and inaccuracy, therefore increasing the likelihood of everytumour or growth to be measured. In some cases, an appropriate treatmentplan/dose can then be administered to the tumour 205.

After an iteration of time, a follow-up medical scan is performed 206.The system seeks to identify the previously identified tumours from thenew follow-up scans by carrying out the previously mentioned steps 207.In addition, the system also identifies any additional growths that mayhave developed in the new follow-up scan. The relative positions ofprevious landmarks, in particular the centre of mass of each landmark,is used to identify the same growths between scans.

If any tumours/growths are identified in the follow-up scan, themeasurements are again digitally analysed and recorded across one ormore dimensions 208. Once measurements for the follow-up scan arerecorded, they are compared to the measurements of the tumour/growth inthe previous scan to determine whether the tumour size has changed 209.The size of the tumour that is being compared is the volume of the oreach tumour between scans.

Similar to FIG. 1, if the tumour has decreased in size, the system willproceed with the same course of treatment until the patient is cured210. However, if the tumour size has not decreased, the system willpursue an alternative treatment method before administering the newtreatment 211. The loop of the treatment phase will continue until thepatient is cured.

Machine learning is the field of study where a computer or computerslearn to perform classes of tasks using the feedback generated from theexperience or data gathered that the machine learning process acquiresduring computer performance of those tasks.

Typically, machine learning can be broadly classed as supervised andunsupervised approaches, although there are particular approaches suchas reinforcement learning and semi-supervised learning which havespecial rules, techniques and/or approaches. Supervised machine learningis concerned with a computer learning one or more rules or functions tomap between example inputs and desired outputs as predetermined by anoperator or programmer, usually where a data set containing the inputsis labelled.

Unsupervised learning is concerned with determining a structure forinput data, for example when performing pattern recognition, andtypically uses unlabelled data sets. Reinforcement learning is concernedwith enabling a computer or computers to interact with a dynamicenvironment, for example when playing a game or driving a vehicle.

Various hybrids of these categories are possible, such as“semi-supervised” machine learning where a training data set has onlybeen partially labelled. For unsupervised machine learning, there is arange of possible applications such as, for example, the application ofcomputer vision techniques to image processing or video enhancement.Unsupervised machine learning is typically applied to solve problemswhere an unknown data structure might be present in the data. As thedata is unlabelled, the machine learning process is required to operateto identify implicit relationships between the data for example byderiving a clustering metric based on internally derived information.For example, an unsupervised learning technique can be used to reducethe dimensionality of a data set and attempt to identify and modelrelationships between clusters in the data set, and can for examplegenerate measures of cluster membership or identify hubs or nodes in orbetween clusters (for example using a technique referred to as weightedcorrelation network analysis, which can be applied to high-dimensionaldata sets, or using k-means clustering to cluster data by a measure ofthe Euclidean distance between each datum).

Semi-supervised learning is typically applied to solve problems wherethere is a partially labelled data set, for example where only a subsetof the data is labelled. Semi-supervised machine learning makes use ofexternally provided labels and objective functions as well as anyimplicit data relationships. When initially configuring a machinelearning system, particularly when using a supervised machine learningapproach, the machine learning algorithm can be provided with sometraining data or a set of training examples, in which each example istypically a pair of an input signal/vector and a desired output value,label (or classification) or signal. The machine learning algorithmanalyses the training data and produces a generalised function that canbe used with unseen data sets to produce desired output values orsignals for the unseen input vectors/signals. The user needs to decidewhat type of data is to be used as the training data, and to prepare arepresentative real-world set of data. The user must however take careto ensure that the training data contains enough information toaccurately predict desired output values without providing too manyfeatures (which can result in too many dimensions being considered bythe machine learning process during training, and could also mean thatthe machine learning process does not converge to good solutions for allor specific examples). The user must also determine the desiredstructure of the learned or generalised function, for example whether touse support vector machines or decision trees.

The use of unsupervised or semi-supervised machine learning approachesare sometimes used when labelled data is not readily available, or wherethe system generates new labelled data from unknown data given someinitial seed labels.

Machine learning may be performed through the use of one or more of: anon-linear hierarchical algorithm; neural network; convolutional neuralnetwork; recurrent neural network; long short-term memory network;multi-dimensional convolutional network; a memory network; fullyconvolutional network or a gated recurrent network allows a flexibleapproach when generating the predicted block of visual data. The use ofan algorithm with a memory unit such as a long short-term memory network(LSTM), a memory network or a gated recurrent network can keep the stateof the predicted blocks from motion compensation processes performed onthe same original input frame. The use of these networks can improvecomputational efficiency and also improve temporal consistency in themotion compensation process across a number of frames, as the algorithmmaintains some sort of state or memory of the changes in motion. Thiscan additionally result in a reduction of error rates.

Developing a machine learning system typically consists of two stages:(1) training and (2) production. During the training the parameters ofthe machine learning model are iteratively changed to optimise aparticular learning objective, known as the objective function or theloss. Once the model is trained, it can be used in production, where themodel takes in an input and produces an output using the trainedparameters.

Any system feature as described herein may also be provided as a methodfeature, and vice versa. As used herein, means plus function featuresmay be expressed alternatively in terms of their correspondingstructure.

Any feature in one aspect may be applied to other aspects, in anyappropriate combination. In particular, method aspects may be applied tosystem aspects, and vice versa. Furthermore, any, some and/or allfeatures in one aspect can be applied to any, some and/or all featuresin any other aspect, in any appropriate combination.

It should also be appreciated that particular combinations of thevarious features described and defined in any aspects can be implementedand/or supplied and/or used independently.

1. A method for determining volumetric properties of one or more lesionsin medical images, the method comprising: receiving image data;determining one or more locations of the one or more lesions in theimage data; creating an image segmentation comprising the determined oneor more locations of the one or more lesions in the image data; andusing the image segmentation to determine a volumetric property of thelesion.
 2. The method of claim 1 wherein determining one or morelocations of the one or more lesions comprises one or both of:identifying a focal point of the one or more lesions; and/or identifyinga focal point of one or more anatomical landmarks.
 3. The method ofclaim 2 wherein the focal point of the one or more lesions comprises acentre of mass for the one or more lesions, and/or the focal point ofthe one or more anatomical landmarks comprises a centre of mass for theone or more lesions.
 4. (canceled)
 5. (canceled)
 6. The method of claim1 wherein determining one or more locations of the one or more lesionscomprises determining the location relative to one or more anatomicallandmarks.
 7. The method of claim 6 wherein the one or more anatomicallandmarks comprise any one of: spine, ribs, lungs, heart, liver andkidneys.
 8. The method of claim 1 wherein determining one or morelocations of the one or more lesions comprises: identifying a focalpoint of the one or more lesions; identifying a focal point of the oneor more anatomical landmarks; and determining the location of the one ormore lesions relative to one or more anatomical landmarks.
 9. The methodof claim 1 wherein the image data comprises any one of or anycombination of: CT scan data; a DICOM image file; a sequence of imagesof sequential slices of anatomy; one or more grayscale images;demographic patient information; prior imaging data; one or more images.10. (canceled)
 11. (canceled)
 12. (canceled)
 13. (canceled)
 14. Themethod of claim 1 further comprising: segmenting the one or more lesionsto create lesion segmentation data; and storing the lesion segmentationdata in the image segmentation.
 15. The method of claim 14 wherein theimage segmentation comprises a mask or contour.
 16. The method of claim1 further comprising: measuring the one or more lesions to create lesionmeasurement data; and storing the lesion segmentation data in an imagemask.
 17. The method of claim 1 further comprising pre-processing theimage data, wherein pre-processing comprises reading the image data andstoring the image data in a memory, wherein the image data is stored inthe memory as at least a four-dimensional floating-point tensor whereinthe dimensions comprise: height, width, batch size, and channels,wherein the channels comprise one or more contrast windows and/orcontrast values.
 18. (canceled)
 19. (canceled)
 20. The method of claim 1wherein determining one or more locations of the one or more lesions inthe image data comprises using a fully convolutional neural network. 21.The method of claim 20 wherein the fully convolutional neural network istrained using backpropagation; and/or a loss function for dense trainingis the sum over the spatial dimensions of the loss functions of theindividual pixels.
 22. The method of claim 21 further comprises:creating one or more heat maps to indicate areas within respective oneor more images of the image data having a high probability of being atumour; and post-processing the one or more heat maps by feeding the oneor more heat maps through a conditional random field method. 23.(canceled)
 24. The method of claim 1 further comprising post-processingthe determined one or more locations of the one or more lesions in theimage data by feeding the determined one or more locations through aconditional random field method.
 25. The method of claim 1 furthercomprising one or more of: determining one dimensional measurements offound lesions, wherein the one-dimensional measurement comprises largestdiameter; or perpendicular diameter; determining two-dimensionalmeasurements of found lesions, optionally wherein the two-dimensionalmeasurements comprises lesion area; determining a three-dimensionalmodel of found lesions with interpolation between 2D slices; determininga largest diameter in three-dimensional space; determining a volume anda surface in three-dimensional space; determining the extent of necrosiswith 1D and/or 2D and/or 3D measurements; and determining disease stagebased on any one of or any combination of lesion localizations, contextvariables, classifications, measurements, numbers and/or summarystatistics of all these.
 26. (canceled)
 27. (canceled)
 28. (canceled)29. (canceled)
 30. (canceled)
 31. The method of claim 1 furthercomprising one or both of: selecting target lesions according to apredetermined criteria, wherein the predetermined criteria comprises anyone of or any combination of: malignancy, size, location, necrosis,other classification, and/or selecting a number of target lesions basedon a human-given or computer-given or computer-optimized probabilitythreshold(s), yielding a number of trackable target lesions ofpotentially varying classes and/or varying locations and/or varyingsizes.
 32. (canceled)
 33. The method of claim 1 further comprisinglocating and identifying the same lesion between scans taken atdifferent time points using landmarks identified by computer and/orhuman.
 34. (canceled)
 35. (canceled)
 36. (canceled)
 37. (canceled) 38.(canceled)
 39. (canceled)
 40. (canceled)
 41. (canceled)
 42. A system fordetermining volumetric properties of one or more lesions in medicalimages, the system: a memory including instructions; and one or moreprocessors configured to execute the instructions to receive image data;determine one or more locations of the one or more lesions in the imagedata; create an image segmentation comprising the determined one or morelocations of the one or more lesions in the image data; and use theimage segmentation to determine a volumetric property of the lesion. 43.A computer program product including one or more non-transitory machinereadable mediums encoded with instructions that when executed by one ormore processors cause a process to be carried out for determiningvolumetric properties of one or more lesions in medical images, theprocess comprising: receiving image data; determining one or morelocations of the one or more lesions in the image data; creating animage segmentation comprising the determined one or more locations ofthe one or more lesions in the image data; and using the imagesegmentation to determine a volumetric property of the lesion.